Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume

The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the-art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline's clinical application.Comment: In the Proceedings of the 16th International Conference of Machine Vision (ICMV 2023), November 15-18, Yerevan, Armeni

Cardiac magnetic resonance for ventricular arrhythmias: a systematic review and meta-analysis

Background: Cardiac magnetic resonance (CMR) allows comprehensive myocardial tissue characterisation, revealing areas of myocardial inflammation or fibrosis that may predispose to ventricular arrhythmias (VAs). With this study, we aimed to estimate the prevalence of structural heart disease (SHD) and decipher the prognostic implications of CMR in selected patients presenting with significant VAs. Methods: Electronic databases were searched for studies enrolling adult patients that underwent CMR for diagnostic or prognostic purposes in the setting of significant VAs. A random effects model meta-analysis of proportions was performed to estimate the prevalence of SHD. HRs were pooled together in order to evaluate the prognostic value of CMR. Results: The prevalence of SHD was reported in 18 studies. In all-comers with significant VAs, the pooled rate of SHD post-CMR evaluation was 39% (24% in the subgroup of premature ventricular contractions and/or non-sustained ventricular tachycardia vs 63% in the subgroup of more complex VAs). A change in diagnosis after use of CMR ranged from 21% to 66% with a pooled average of 35% (29%–41%). A non-ischaemic cardiomyopathy was the most frequently identified SHD (56%), followed by ischaemic heart disease (21%) and hypertrophic cardiomyopathy (5%). After pooling together data from six studies, we found that the presence of late gadolinium enhancement was associated with increased risk of major adverse outcomes in patients with significant VAs (pooled HR: 1.79; 95% CI 1.33 to 2.42). Conclusion: CMR is a valuable tool in the diagnostic and prognostic evaluation of patients with VAs. CMR should be considered early after initial evaluation in the diagnostic algorithm for VAs of unclear aetiology as this strategy may also define prognosis and improve risk stratification

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis

Impact of induction therapy on outcomes after heart transplantation

Background Approximately 50% of heart transplant (HT) programs utilize induction therapy (IT) with interleukin-2 receptor antagonists (IL2RA) or polyclonal anti-thymocyte antibodies (ATG). Methods Adult HT recipients were identified in the UNOS Registry between 2010 and 2020. We compared mortality between IT strategies with competing risk analysis. Results A total of 28 634 HT recipients were included in the study (50.1% no IT, 21.3% ATG, 27.9% IL2RA, .7% alemtuzumab, .01% OKT3). Adjusted all-cause, 30 day and 1 year mortality were lower among those treated with IT than no IT (sub-hazard ratio [SHR] .87, 95% CI .79-.96, SHR .86, .76-.97, SHR .76, .63-.93, P = .007, respectively). In propensity score matching analysis IT was associated with lower 30-day and 1-year mortality. IL2RA had higher all-cause and 1-year mortality than ATG (SHR 1.41, 95% CI 1.23-1.69 and 1.55, 95% CI 1.29-1.88, respectively). Utilization of IT was associated with significantly lower risk of treated rejection at 1 year after HT compared with no IT (relative risk ratio [RRR] .79) and similarly ATG compared with IL2RA (RRR .51). Conclusion IT was associated with lower mortality and treated rejection episodes than no IT. IL2RA is the most used IT approach but ATG has lower risk of treated rejection and mortality

State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

Purpose We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). Methods and results We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 +/- 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors. ML models for 3- and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. Conclusion Machine learning models showed good predictive accuracy of outcomes after heart transplantation

An updated meta-analysis of MitraClip versus surgery for mitral regurgitation

Background: Although studies demonstrate its feasibility, there is ongoing debate on the short and long-term outcomes of MitraClip versus surgical repair or mitral valve replacement (MVR). The objective of this meta-analysis is to compare the safety, morbidity, mortality and long-term function following MitraClip compared to MVR.Methods: Articles were searched in PubMed and Cochrane databases for studies comparing outcomes of MitraClip and surgery on December 1, 2019. Eligible prospective, retrospective, randomized and non-randomized studies were reviewed.Results: A total of nine studies (n=1,873, MitraClip =533, MVR =644) were eligible for review. At baseline, MitraClip patients had more comorbidities than MVR patients, including myocardial infarction (P<0.001), chronic obstructive pulmonary disease (P=0.022) and chronic kidney disease (P<0.001). MitraClip was associated with shorter length of stay (-3.86 days; 95% CI, -4.73 to -2.99; P<0.01) with a similar safety profile. Residual moderate-to-severe mitral regurgitation was more frequent in MitraClip at discharge (OR, 2.81; 95% CI, 1.39-5.69; P<0.01) and at five years (OR, 2.46; 95% CI, 1.54-3.94; P<0.01), and there was a higher need for reoperation on the MitraClip group at latest follow-up (OR, 5.28; 95% CI, 3.43-8.11; P<0.01). The overall mortality was comparable between the two groups (HR, 2.06; 95% CI, 0.98-4.29; P=0.06) for a mean follow-up of 4.8 years.Conclusions: Compared to surgery, MitraClip demonstrates a similar safety profile and shorter length of stay in high-risk patients, at the expense of increased residual mitral regurgitation and higher reoperation rate. Despite this, long term mortality appears comparable between the two techniques, suggesting that a patient-tailored approach will lead to optimal results

Impact of paravalvular leak on left ventricular remodeling and global longitudinal strain 1 year after transcatheter aortic valve replacement

Background:New mild or persistent moderate paravalvular leak (PVL) is a known predictor of poor outcomes after transcatheter aortic valve replacement (TAVR). Its impact on left ventricular (LV) remodeling and global longitudinal strain (GLS) has not been well studied.Materials & methods:We collected echocardiographic data in 99 TAVR patients. LV remodeling and GLS were compared between patients with and without PVL.Results:Patients without PVL (n = 84) had significant LV ejection fraction, wall thickness and LV mass improvement compared with patients with PVL (n = 15; p < 0.001 for all). Diastolic function worsened in patients with PVL. Baseline GLS improved significantly regardless of PVL (p = 0.016 and p = 0.01, respectively) and was not predictive of LV ejection fraction or LV mass improvement when analyzed in tertiles.Conclusion:PVL impedes reverse LV remodeling but not GLS improvement 1-year after TAVR. Baseline GLS was not a predictor of LV remodeling